Neuro-Evolved Heuristics for Variable Gapped Common Subsequence Identification

• URL: http://arxiv.org/abs/2608.00888v1
• Date: Sat, 01 Aug 2026 22:36:35 GMT
• Title: Neuro-Evolved Heuristics for Variable Gapped Common Subsequence Identification
• Abstract summary: This study addresses the Variable Gapped Longest Common Subsequence Problem (VGLCSP)<n>VGLCSP is a variant of the classical longest common subsequence problem with additional gap constraints.<n>We propose a learning-based approach for automatically designing more effective data-driven robustnesss.
• Score: 40.920500181764986
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study addresses the Variable Gapped Longest Common Subsequence Problem (VGLCSP), a variant of the classical longest common subsequence problem with additional gap constraints and applications in sequence alignment and time-series analysis. While the two-sequence version has been widely studied using dynamic programming, the generalized multi-sequence form is usually solved with beam search-based heuristics, whose hand-crafted designs often lack robustness. To overcome this limitation, we propose a learning-based approach for automatically designing more effective data-driven heuristics. The heuristics are represented by a neural network with predefined architecture, whose weights are optimized by a genetic algorithm within a neuro-evolutionary framework. The learning process alternates between weight optimization and evaluation within an iterative multi-source beam search procedure, a state-of-the-art method for the problem. Rather than constructing solutions directly, the neural network learns to guide the search process, producing a neuro-evolved heuristic. We further introduce an ensemble heuristic that combines the scores of learned and the best-performing hand-crafted heuristic. Integrated into the iterative multi-source beam search framework, the resulting hybrid approach outperforms existing methods on both synthetic benchmark instances and newly introduced real-world instances with data-driven gap constraints.

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